[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"health-study-detail:100613999":3},{"organization":4,"armGroups":7,"interventions":27,"overallOfficials":32,"centralContacts":32,"locations":32,"responsibleParty":33,"collaborators":32,"id":35,"slug":36,"hasResults":37,"nctId":38,"briefTitle":39,"officialTitle":40,"acronym":32,"eligibilityCriteria":41,"healthyVolunteers":37,"sex":42,"minAge":43,"maxAge":44,"enrollmentInfo":45,"targetDuration":32,"studyType":48,"phases":49,"briefSummary":51,"conditions":52,"keywords":56,"overallStatus":60,"whyStopped":32,"lastUpdateSubmitDate":61,"lastUpdatePostDateStruct":62,"startDateStruct":65,"completionDateStruct":66,"leadSponsor":68,"locationsCount":32},{"fullName":5,"class":6},"The First Affiliated Hospital of Anhui Medical University","OTHER",[8,14,19,23],{"label":9,"type":10,"description":11,"interventionNames":12},"Group A","SHAM_COMPARATOR","During colonoscopy intubation, AI system is used to calculate and analyze \"caecal intubation time,\" \"red-out percentage,\" and the \"AI-based red-out avoiding score\" in real-time; however, these results are not provided as feedback to the operating colonoscopist.",[13],"Other: AI system",{"label":15,"type":16,"description":17,"interventionNames":18},"Group B","EXPERIMENTAL","During colonoscopy intubation, AI system is used to calculate and analyze \"caecal intubation time,\" \"red-out percentage,\" and the \"AI-based red-out avoiding score\" in real-time, with only the caecal intubation time being provided as feedback to the operator, while the red-out percentage and AI-based red-out avoiding score are withheld.",[13],{"label":20,"type":16,"description":21,"interventionNames":22},"Group C","During colonoscopy intubation, AI system is used to calculate and analyze \"caecal intubation time,\" \"red-out percentage,\" and the \"AI-based red-out avoiding score\" in real-time, with only the red-out percentage being provided as feedback to the operator, while the caecal intubation time and AI-based red-out avoiding score are withheld.",[13],{"label":24,"type":16,"description":25,"interventionNames":26},"Group D","During colonoscopy intubation, AI system is used to calculate and analyze the \"caecal intubation time\" \"red-out percentage,\" and \"AI-based red-out avoiding score\" in real-time, with all three results provided as feedback to the operating colonoscopist.",[13],[28],{"type":6,"name":29,"description":30,"armGroupLabels":31,"otherNames":32},"AI system","AI-system Performance Feedback in group B, group C, and group D.",[9,15,20,24],null,{"type":34,"investigatorFullName":32,"investigatorTitle":32,"investigatorAffiliation":32,"oldNameTitle":32,"oldOrganization":32},"SPONSOR","100613999","real-time-feedback-of-red-out-within-colonoscopy-intubation-100613999",false,"NCT07273890","Real-time Feedback of Red-out Within Colonoscopy Intubation","Prospective, Multicenter, Controlled Study on the Impact of Real-time Feedback on Red-out","Inclusion Criteria:\n\n1. Study Participants (Patients):\n\n   Aged 18 to 70 years, any gender. Individuals scheduled to undergo diagnostic or screening colonoscopy at the investigational site.\n2. Colonoscopists:\n\nExpert-level colonoscopists (having performed a total of \\>1000 colonoscopy procedures).\n\nRight-handed.\n\nExclusion Criteria:\n\n1. Study Participants (Patients):\n\n   Individuals undergoing the following procedures:\n\n   cases with a history of colorectal surgery; cases with a history of chemotherapy, raditherapy; cases with a history of abdominal, and\u002For pelvic surgery; cases with a history of difficult colonoscopies; cases with colorectal tumours and obstructive lesions; cases with colorectal diverticula; cases with ulcerative colitis or Crohn's disease; cases with ischemic bowel disease; cases with colorectal polyposis; cases with melanosis coli; cases undergoing sigmoidoscopy; cases with poor intestional cleanliness (segment Boston bowel preparation scale (BBPS) of \\\u003C 2 points, total BBPS of \\\u003C 6 points); cases undergoing therapy procedures such as biopsy or CSP during the intubation phase; cases with transparent cap assisted colonoscopy; cases with water-assisted colonoscopy; cases with air insufflation level of M or L; cases failed caecal intubation within 15 min; cases with colonoscope stiffness level \\> 0; obese cases or underweight cases; and cases refusing participation.\n\n   Individuals who decline to provide informed consent.\n2. Colonoscopists:\n\nThose who have performed fewer than 300 complete colonoscopies in any calendar year within the past three years.\n\nThose who decline to participate in the study.","ALL","18 Years","70 Years",{"count":46,"type":47},576,"ESTIMATED","INTERVENTIONAL",[50],"NA","This study will employ a prospective, multicenter, controlled design. It will be conducted across multiple centers, with participated centers randomly assigned to one of four groups: Group A, Group B, Group C, and Group D.\n\nThe research will primarily focus on the AI-based analysis of colonoscopic images to calculate the following metrics: caecal intubation time, red-out percentage, and the AI-based red-out avoiding score. Based on the study's implementation protocol, a decision will be made regarding whether to provide real-time feedback. Additionally, the presence of any complications will be assessed both during and after the colonoscopy procedure.",[53,54,55],"Artificial Intelligence","Colonoscopy","Real-time Feedback",[54,57,58,59],"Intubation","Real-time feedback","Red-out","NOT_YET_RECRUITING","2025-11-27",{"date":63,"type":64},"2025-12-10","ACTUAL",{"date":63,"type":47},{"date":67,"type":47},"2028-04-20",{"name":5,"class":6}]